This project deals with the detection of anomalies in seabed surveys collected using multibeam sonar. Detecting anomalies is a common task in the world of signal processing in general and in image processing in particular. Therefore, there are established methods and methodologies for dealing with such problems in these worlds.
As oppose to typical data in these fields, information collected by the multibeam sonar presents various problems that are unique - sampling on irregular grids, variability of the sampling nature depending on the position of the boat and the depth of the soil and areas lacking samples. The presence of these unique problems does not enable immediate implementation of existing methods.
In this project, we reviewed solutions from different fields and generalized methods for identifying anomalies and improving the data collected by the sonar. Our solution proposes an innovative method for detecting "holes", areas lacking in samples, that combines methods from the realm of cosmology into signal processing. Then, interpolating the missing data using a novel multiscale iterative interpolation method. Finally, we Implemented an algorithm we developed for anomalies detection based on local sparse characteristics.
- A winner of the Roland Wilk award, 2017
- Citation in EE annual project contest, 2017
Dr. Alon Amar